mixture experiment and cost function

[#1]

Hello,

I have a mixture data with ingredients I_1, I_2, ... I_9. They add up to 1 as this is a mixture data. The response is y. Of the 9 ingredients 6 of them have cost associated with them, say I_1 - I_6. I creates a new column: C = p1*I_1 + p2*I_2 + ... + p6*I_6. This gives the total cost per unit weight. Now I run a mixture response surface model for y. But when I go to optimize it, I like to take into account the cost column C and find the optimal solution while also minimizing the cost C.

How would I do this? 

Thanks,

[#2]

I noticed that not every row in the data table added to 1. They are close, but not exact. I am wondering if this is causing the issues that you see. For example, the column properties have X1 ranging from 0.3 to 0.7.  But the actual ranges are from approximately 0.32 to 0.73489. 

These differences might seem small, but I do wonder if it is causing some of the issues that you are finding. If a row does not add to 1, then JMP will automatically rescale the values so that they do (I believe that is what is happening now). Therefore the ranges may be different than what you provided. 

[#3]

Sorry to interrupt the discussion, but why are you using stepwise for a mixture design?  You should have a model in mind á priori.  All analysis are contingent on the model.  If you change what is or is not in the model, the analysis will change.